Removal of carbamazepine and clofibric acid from water using double templates–molecularly imprinted polymers

A novel double templates–molecularly imprinted polymer (MIP) was prepared by precipitation polymerization using carbamazepine (CBZ) and clofibric acid (CA) as the double templates molecular and 2-vinylpyridine as functional monomer. The equilibrium data of MIP was well described by the Freundlich isotherm model. Two kinetic models were adopted to describe the experimental data, and the pseudo second-order model well-described adsorption of CBZ and CA on the MIP. Adsorption experimental results showed that the MIP had good selectivity and adsorption capacity for CBZ and CA in the presence of competitive compounds compared with non-imprinted polymer, commercial powdered activated carbon, and C18 adsorbents. The feasibility of removing CBZ and CA from water by the MIP was demonstrated using tap water, lake water, and river water.     

有机化合物动态定量结构—生物降解关系（QSBR）模型研究

本文通过分析影响有机物生物降解的基本因素，指出ＱＳＢＲ模型应综合反映影响有机物生物降解的四个方面：取代化合物的摄入、取代化合物的诱导作用，毒性物质的形成和基础酶的缺乏，在此基础之上给出了动态ＱＳＢＲ的概念模型及方法学基础，并利用其研究了氯代芳香化合物的生物降解性。     

Research on Mechanism of Diversified Investment and Financing in Building Ecological Cities

Ecological city is the advanced mode of harmonious development of city. Constant improvement is being made in the development of ecological city. However, building ecological city is an enormous project that calls for a huge amount of capital. The government fund is far from adequate. This paper probes into the fund issue and discusses in detail the mechanism of investment and financing in building ecological city from various aspects such as diversification of the investment subjects and the modes of financing.     

Integrating classification and regression tree (CART) with GIS for assessment of heavy metals pollution

The classification and regression tree (CART) model integrated with geographical information systems and the assessment of heavy-metals pollution system was developed to assess the heavy metals pollution in Fuyang, Zhejiang, China. The integration of the decision tree model with ArcGIS Engine 9 using a COM implementation in Microsoft® Visual Basic 6.0 provided an approach for assessing the spatial distribution of soil Zn content with high predictive accuracy. The Zn concentration classes estimated by CART assigned the right classes with an accuracy of near 90%. This is a great improvement compared to the ordinary Kriging method for the spatial autocorrelation of the study area severely destroyed by human activities. Also, it can be used to investigate the inter-relationships between the heavy metals pollution and environmental and anthropogenic variables. Moreover, the research presents model predictions over space for further applications and investigations.     

Efficiencies of different microbial parameters as indicator to assess slight metal pollutions in a farm field near a gold mining area

In order to monitor changes in the concentrations of metals in the soil, different microbial indices such as BIOLOG^®, microbial carbon (C_mic), basal respiration, and culturable microbe’s most probable number were used. We compared these methods and wanted to discover which method was the best at measuring slight changes in the amounts of heavy metals. Factor analyses were applied to the BIOLOG^® data and metal concentrations so the combined effects of heavy metals on microbes could be analyzed via statistical data reduction and the distribution patterns of metal concentration could also be revealed. The results showed that the BIOLOG^® method could barely detect subtle characteristic changes in the soil samples, while the C_mic method was more sensitive. Furthermore, different heavy metals did not have the same origin/source, and their effects on microbial indices should be analyzed separately. Significant positive correlations between C_mic and metals were observed and suggested the limitation of using traditional microbial parameters as metal pollution indicators. Among all the soil characteristics in our study, pH seemed to be the most active abiotic factor that affected microorganisms.     

Anaerobic digestion combined pyrolysis for paper mill sludge disposal and its influence on char characteristics

Journal of Material Cycles and Waste Management - The disposal of paper mill sludge through anaerobic digestion (AD) combined pyrolysis was investigated. Two paper mill sludge samples, both with...     

Speciation and quantification of vapor phases in soy biodiesel and waste cooking oil biodiesel

This study characterizes the compositions of two biodiesel vapors, soy biodiesel and waste cooking oil biodiesel, to provide a comprehensive understanding of biodiesels. Vapor phases were sampled by purging oil vapors through thermal desorption tubes which were then analyzed by the thermal desorption/GC/MS system. The results show that the compounds of biodiesel vapors can be divided into four groups. They include methyl esters (the main biodiesel components), oxygenated chemicals, alkanes and alkenes, and aromatics. The first two chemical groups are only found in biodiesel vapors, not in the diesel vapor emissions. The percentages of mean concentrations for methyl esters, oxygenated chemicals, alkanes and alkenes, and aromatics are 66.1%, 22.8%, 4.8% and 6.4%, respectively for soy biodiesel, and 35.8%, 35.9%, 27.9% and 0.3%, respectively for waste cooking oil biodiesel at a temperature of 25 ± 2 °C. These results show that biodiesels have fewer chemicals and lower concentrations in vapor phase than petroleum diesel, and the total emission rates are between one-sixteenth and one-sixth of that of diesel emission, corresponding to fuel evaporative emissions of loading losses of between 106 μg l⁻¹ and 283 μg l⁻¹. Although diesels generate more vapor phase emissions, biodiesels still generate considerable amount of vapor emissions, particularly the emissions from methyl esters and oxygenated chemicals. These two chemical groups are more reactive than alkanes and aromatics. Therefore, speciation and quantification of biodiesel vapor phases are important.     

孔石莼和重金属（铜与镉）对海水无机碳体系影响的模拟研究

通过室内实验模拟并应用海洋生态系统动力学模型,研究了孔石莼和重金属(铜与镉)共同作用下,海水二氧化碳体系及海洋碳源汇强度的变化过程.结果表明,二氧化碳体系各组分含量随时间的变化幅度(Δ)和趋势与重金属种类和添加浓度有关.与对照相比,低浓度(0.1μmol·L^-1和1μmol·L^-1)的铜、镉添加组中,DIC、HCO₃^-和pCO₂均随时间的延长呈下降趋势(p=0.01).而当铜、镉浓度大于“转折浓度”后,t=7时DIC、HCO₃^-和p_CO2均要大于初始值,其增幅随着重金属浓度的增加而增大.对于Δ由负值过渡到正值所对应的“转折浓度”,铜添加组要明显低于镉添加组.这与孔石莼对不同种类重金属的耐受程度不同有关.此外,当重金属浓度不同时,箱中的碳源汇格局亦随时间做不同的变化.当重金属浓度较低时,箱内水体表现为CO₂的汇,并且随着时间推移,可能过渡成为大气CO₂的源;高浓度重金属的影响下,箱内水体表现为CO₂的源,且随重金属浓度的增大,其向大气释放CO2的量也增加.